In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at Moe's Tavern.
The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like "Moe's Cavern", "Flaming Moe's", "Uncle Moe's Family Feed-Bag", etc..
"""
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"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
Play around with view_sentence_range to view different parts of the data.
view_sentence_range = (0, 10)
"""
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"""
import numpy as np
print('Dataset Stats')
print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))
scenes = text.split('\n\n')
print('Number of scenes: {}'.format(len(scenes)))
sentence_count_scene = [scene.count('\n') for scene in scenes]
print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))
sentences = [sentence for scene in scenes for sentence in scene.split('\n')]
print('Number of lines: {}'.format(len(sentences)))
word_count_sentence = [len(sentence.split()) for sentence in sentences]
print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))
print()
print('The sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
Dataset Stats Roughly the number of unique words: 11492 Number of scenes: 262 Average number of sentences in each scene: 15.248091603053435 Number of lines: 4257 Average number of words in each line: 11.50434578341555 The sentences 0 to 10: Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink. Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch. Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately? Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick. Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self. Homer_Simpson: I got my problems, Moe. Give me another one. Moe_Szyslak: Homer, hey, you should not drink to forget your problems. Barney_Gumble: Yeah, you should only drink to enhance your social skills.
The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:
To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:
vocab_to_intint_to_vocabReturn these dictionaries in the following tuple (vocab_to_int, int_to_vocab)
import numpy as np
import problem_unittests as tests
def create_lookup_tables(text):
"""
Create lookup tables for vocabulary
:param text: The text of tv scripts split into words
:return: A tuple of dicts (vocab_to_int, int_to_vocab)
"""
# TODO: Implement Function
voc = set(text)
voc_to_int = {c: i for i, c in enumerate(voc)}
int_to_voc = dict( enumerate(voc) )
return voc_to_int , int_to_voc
"""
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"""
tests.test_create_lookup_tables(create_lookup_tables)
Tests Passed
We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word "bye" and "bye!".
Implement the function token_lookup to return a dict that will be used to tokenize symbols like "!" into "||Exclamation_Mark||". Create a dictionary for the following symbols where the symbol is the key and value is the token:
This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token "dash", try using something like "||dash||".
def token_lookup():
"""
Generate a dict to turn punctuation into a token.
:return: Tokenize dictionary where the key is the punctuation and the value is the token
"""
# TODO: Implement Function
values = ['||Period||','||Comma||','||Quotation_Mark||','||Semicolon||','||Exclamation_mark||','||Question_mark||','||Left_Parentheses||','||Right_Parentheses||','||Dash||','||Return||']
keys = ['.', ',', '"', ';', '!', '?', '(', ')', '--','\n']
token = dict(zip(keys,values))
return token
"""
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"""
tests.test_tokenize(token_lookup)
Tests Passed
Running the code cell below will preprocess all the data and save it to file.
"""
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"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)
This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.
"""
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"""
import helper
import numpy as np
import problem_unittests as tests
int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
"""
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"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0 Default GPU Device: /gpu:0
Implement the get_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders:
name parameter.Return the placeholders in the following tuple (Input, Targets, LearningRate)
def get_inputs():
"""
Create TF Placeholders for input, targets, and learning rate.
:return: Tuple (input, targets, learning rate)
"""
# TODO: Implement Function
input = tf.placeholder(tf.int32, shape=(None,None), name="input")
targets = tf.placeholder(tf.int32, shape=(None,None))
learning_rate = tf.placeholder(tf.float32, shape=None)
return input, targets, learning_rate
"""
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"""
tests.test_get_inputs(get_inputs)
Tests Passed
Stack one or more BasicLSTMCells in a MultiRNNCell.
rnn_sizezero_state() functiontf.identity()Return the cell and initial state in the following tuple (Cell, InitialState)
def get_init_cell(batch_size, rnn_size):
"""
Create an RNN Cell and initialize it.
:param batch_size: Size of batches
:param rnn_size: Size of RNNs
:return: Tuple (cell, initialize state)
"""
# TODO: Implement Function
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
cell = tf.contrib.rnn.MultiRNNCell([lstm])
initial_state = tf.identity(cell.zero_state(batch_size,tf.int32),name="initial_state")
return cell, initial_state
"""
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"""
tests.test_get_init_cell(get_init_cell)
Tests Passed
Apply embedding to input_data using TensorFlow. Return the embedded sequence.
def get_embed(input_data, vocab_size, embed_dim):
"""
Create embedding for <input_data>.
:param input_data: TF placeholder for text input.
:param vocab_size: Number of words in vocabulary.
:param embed_dim: Number of embedding dimensions
:return: Embedded input.
"""
# TODO: Implement Function
embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))
embed = tf.nn.embedding_lookup(embedding, input_data)
return embed
"""
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"""
tests.test_get_embed(get_embed)
Tests Passed
You created a RNN Cell in the get_init_cell() function. Time to use the cell to create a RNN.
tf.nn.dynamic_rnn()tf.identity()Return the outputs and final_state state in the following tuple (Outputs, FinalState)
def build_rnn(cell, inputs):
"""
Create a RNN using a RNN Cell
:param cell: RNN Cell
:param inputs: Input text data
:return: Tuple (Outputs, Final State)
"""
# TODO: Implement Function
outputs, states = tf.nn.dynamic_rnn(cell, inputs, dtype='float32')
final_state = tf.identity(states, name="final_state")
return outputs, final_state
"""
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"""
tests.test_build_rnn(build_rnn)
Tests Passed
Apply the functions you implemented above to:
input_data using your get_embed(input_data, vocab_size, embed_dim) function.cell and your build_rnn(cell, inputs) function.vocab_size as the number of outputs.Return the logits and final state in the following tuple (Logits, FinalState)
def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):
"""
Build part of the neural network
:param cell: RNN cell
:param rnn_size: Size of rnns
:param input_data: Input data
:param vocab_size: Vocabulary size
:param embed_dim: Number of embedding dimensions
:return: Tuple (Logits, FinalState)
"""
# TODO: Implement Function
embed = get_embed(input_data, vocab_size, rnn_size)
rnn, final_state = build_rnn(cell, embed)
logits = tf.contrib.layers.fully_connected(rnn, vocab_size, activation_fn=None, weights_initializer = tf.truncated_normal_initializer(stddev=0.1), biases_initializer=tf.zeros_initializer())
return logits, final_state
"""
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"""
tests.test_build_nn(build_nn)
Tests Passed
Implement get_batches to create batches of input and targets using int_text. The batches should be a Numpy array with the shape (number of batches, 2, batch size, sequence length). Each batch contains two elements:
[batch size, sequence length][batch size, sequence length]If you can't fill the last batch with enough data, drop the last batch.
For exmple, get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], 3, 2) would return a Numpy array of the following:
[
# First Batch
[
# Batch of Input
[[ 1 2], [ 7 8], [13 14]]
# Batch of targets
[[ 2 3], [ 8 9], [14 15]]
]
# Second Batch
[
# Batch of Input
[[ 3 4], [ 9 10], [15 16]]
# Batch of targets
[[ 4 5], [10 11], [16 17]]
]
# Third Batch
[
# Batch of Input
[[ 5 6], [11 12], [17 18]]
# Batch of targets
[[ 6 7], [12 13], [18 1]]
]
]Notice that the last target value in the last batch is the first input value of the first batch. In this case, 1. This is a common technique used when creating sequence batches, although it is rather unintuitive.
def get_batches(int_text, batch_size, seq_length):
"""
Return batches of input and target
:param int_text: Text with the words replaced by their ids
:param batch_size: The size of batch
:param seq_length: The length of sequence
:return: Batches as a Numpy array
"""
# TODO: Implement Function
n_batches = (len(int_text) - 1) // (batch_size * seq_length)
batches = np.zeros((n_batches, 2, batch_size, seq_length),dtype=np.int32)
for b in range(n_batches):
for j in range(batch_size):
batches[b][0][j] = int_text[seq_length*(n_batches*j+b) : seq_length*(n_batches*j+b+1)]
batches[b][1][j] = int_text[seq_length*(n_batches*j+b)+1 : seq_length*(n_batches*j+b+1)+1]
batches[b][1][batch_size-1][seq_length-1] = batches[0][0][0][0]
return batches
"""
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"""
tests.test_get_batches(get_batches)
Tests Passed
Tune the following parameters:
num_epochs to the number of epochs.batch_size to the batch size.rnn_size to the size of the RNNs.embed_dim to the size of the embedding.seq_length to the length of sequence.learning_rate to the learning rate.show_every_n_batches to the number of batches the neural network should print progress.# Number of Epochs
num_epochs = 80
# Batch Size
batch_size = 100
# RNN Size
rnn_size = 512
# Embedding Dimension Size
embed_dim = 256
# Sequence Length
seq_length = 10
# Learning Rate
learning_rate = 0.001
# Show stats for every n number of batches
show_every_n_batches = 100
"""
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"""
save_dir = './save'
Build the graph using the neural network you implemented.
"""
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"""
from tensorflow.contrib import seq2seq
train_graph = tf.Graph()
with train_graph.as_default():
vocab_size = len(int_to_vocab)
input_text, targets, lr = get_inputs()
input_data_shape = tf.shape(input_text)
cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)
# Probabilities for generating words
probs = tf.nn.softmax(logits, name='probs')
# Loss function
cost = seq2seq.sequence_loss(
logits,
targets,
tf.ones([input_data_shape[0], input_data_shape[1]]))
# Optimizer
optimizer = tf.train.AdamOptimizer(lr)
# Gradient Clipping
gradients = optimizer.compute_gradients(cost)
capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]
train_op = optimizer.apply_gradients(capped_gradients)
"""
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"""
batches = get_batches(int_text, batch_size, seq_length)
with tf.Session(graph=train_graph) as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(num_epochs):
state = sess.run(initial_state, {input_text: batches[0][0]})
for batch_i, (x, y) in enumerate(batches):
feed = {
input_text: x,
targets: y,
initial_state: state,
lr: learning_rate}
train_loss, state, _ = sess.run([cost, final_state, train_op], feed)
# Show every <show_every_n_batches> batches
if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:
print('Epoch {:>3} Batch {:>4}/{} train_loss = {:.3f}'.format(
epoch_i,
batch_i,
len(batches),
train_loss))
# Save Model
saver = tf.train.Saver()
saver.save(sess, save_dir)
print('Model Trained and Saved')
Epoch 0 Batch 0/69 train_loss = 8.856 Epoch 1 Batch 31/69 train_loss = 5.020 Epoch 2 Batch 62/69 train_loss = 4.328 Epoch 4 Batch 24/69 train_loss = 3.769 Epoch 5 Batch 55/69 train_loss = 3.340 Epoch 7 Batch 17/69 train_loss = 2.883 Epoch 8 Batch 48/69 train_loss = 2.577 Epoch 10 Batch 10/69 train_loss = 2.061 Epoch 11 Batch 41/69 train_loss = 1.907 Epoch 13 Batch 3/69 train_loss = 1.565 Epoch 14 Batch 34/69 train_loss = 1.353 Epoch 15 Batch 65/69 train_loss = 1.218 Epoch 17 Batch 27/69 train_loss = 1.005 Epoch 18 Batch 58/69 train_loss = 0.839 Epoch 20 Batch 20/69 train_loss = 0.723 Epoch 21 Batch 51/69 train_loss = 0.610 Epoch 23 Batch 13/69 train_loss = 0.547 Epoch 24 Batch 44/69 train_loss = 0.566 Epoch 26 Batch 6/69 train_loss = 0.490 Epoch 27 Batch 37/69 train_loss = 0.494 Epoch 28 Batch 68/69 train_loss = 0.485 Epoch 30 Batch 30/69 train_loss = 0.501 Epoch 31 Batch 61/69 train_loss = 0.381 Epoch 33 Batch 23/69 train_loss = 0.411 Epoch 34 Batch 54/69 train_loss = 0.388 Epoch 36 Batch 16/69 train_loss = 0.433 Epoch 37 Batch 47/69 train_loss = 0.400 Epoch 39 Batch 9/69 train_loss = 0.434 Epoch 40 Batch 40/69 train_loss = 0.392 Epoch 42 Batch 2/69 train_loss = 0.439 Epoch 43 Batch 33/69 train_loss = 0.432 Epoch 44 Batch 64/69 train_loss = 0.398 Epoch 46 Batch 26/69 train_loss = 0.403 Epoch 47 Batch 57/69 train_loss = 0.401 Epoch 49 Batch 19/69 train_loss = 0.377 Epoch 50 Batch 50/69 train_loss = 0.426 Epoch 52 Batch 12/69 train_loss = 0.417 Epoch 53 Batch 43/69 train_loss = 0.405 Epoch 55 Batch 5/69 train_loss = 0.420 Epoch 56 Batch 36/69 train_loss = 0.380 Epoch 57 Batch 67/69 train_loss = 0.384 Epoch 59 Batch 29/69 train_loss = 0.439 Epoch 60 Batch 60/69 train_loss = 0.359 Epoch 62 Batch 22/69 train_loss = 0.412 Epoch 63 Batch 53/69 train_loss = 0.393 Epoch 65 Batch 15/69 train_loss = 0.420 Epoch 66 Batch 46/69 train_loss = 0.425 Epoch 68 Batch 8/69 train_loss = 0.376 Epoch 69 Batch 39/69 train_loss = 0.392 Epoch 71 Batch 1/69 train_loss = 0.417 Epoch 72 Batch 32/69 train_loss = 0.381 Epoch 73 Batch 63/69 train_loss = 0.415 Epoch 75 Batch 25/69 train_loss = 0.414 Epoch 76 Batch 56/69 train_loss = 0.396 Epoch 78 Batch 18/69 train_loss = 0.355 Epoch 79 Batch 49/69 train_loss = 0.316 Model Trained and Saved
Save seq_length and save_dir for generating a new TV script.
"""
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"""
# Save parameters for checkpoint
helper.save_params((seq_length, save_dir))
"""
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"""
import tensorflow as tf
import numpy as np
import helper
import problem_unittests as tests
_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
seq_length, load_dir = helper.load_params()
Get tensors from loaded_graph using the function get_tensor_by_name(). Get the tensors using the following names:
Return the tensors in the following tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
def get_tensors(loaded_graph):
"""
Get input, initial state, final state, and probabilities tensor from <loaded_graph>
:param loaded_graph: TensorFlow graph loaded from file
:return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
"""
# TODO: Implement Function
input = loaded_graph.get_tensor_by_name('input:0')
initial_state = loaded_graph.get_tensor_by_name('initial_state:0')
final_state = loaded_graph.get_tensor_by_name('final_state:0')
probs = loaded_graph.get_tensor_by_name('probs:0')
return input, initial_state, final_state, probs
"""
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"""
tests.test_get_tensors(get_tensors)
Tests Passed
Implement the pick_word() function to select the next word using probabilities.
def pick_word(probabilities, int_to_vocab):
"""
Pick the next word in the generated text
:param probabilities: Probabilites of the next word
:param int_to_vocab: Dictionary of word ids as the keys and words as the values
:return: String of the predicted word
"""
# TODO: Implement Function
print(int_to_vocab)
print(probabilities)
x=np.random.choice(len(probabilities), 1, p=probabilities)
return int_to_vocab[x[0]]
"""
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"""
tests.test_pick_word(pick_word)
{0: 'this', 1: 'is', 2: 'a', 3: 'test'}
[ 0.1 0.8 0.05 0.05]
Tests Passed
This will generate the TV script for you. Set gen_length to the length of TV script you want to generate.
gen_length = 200
# homer_simpson, moe_szyslak, or Barney_Gumble
prime_word = 'moe_szyslak'
"""
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"""
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load saved model
loader = tf.train.import_meta_graph(load_dir + '.meta')
loader.restore(sess, load_dir)
# Get Tensors from loaded model
input_text, initial_state, final_state, probs = get_tensors(loaded_graph)
# Sentences generation setup
gen_sentences = [prime_word + ':']
prev_state = sess.run(initial_state, {input_text: np.array([[1]])})
# Generate sentences
for n in range(gen_length):
# Dynamic Input
dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]
dyn_seq_length = len(dyn_input[0])
# Get Prediction
probabilities, prev_state = sess.run(
[probs, final_state],
{input_text: dyn_input, initial_state: prev_state})
pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)
gen_sentences.append(pred_word)
# Remove tokens
tv_script = ' '.join(gen_sentences)
for key, token in token_dict.items():
ending = ' ' if key in ['\n', '(', '"'] else ''
tv_script = tv_script.replace(' ' + token.lower(), key)
tv_script = tv_script.replace('\n ', '\n')
tv_script = tv_script.replace('( ', '(')
print(tv_script)
{0: 'pocket', 1: 'wear', 2: 'desire', 3: "foolin'", 4: 'dryer', 5: 'rude', 6: 'effervescence', 7: 'talked', 8: 'parrot', 9: 'crystal', 10: 'wishes', 11: 'contemporary', 12: 'evergreen', 13: 'stink', 14: 'glass', 15: 'exploiter', 16: 'glen:', 17: 'peppy', 18: 'bon-bons', 19: 'arrested:', 20: 'apartment', 21: 'sunny', 22: 'holds', 23: 'nudge', 24: 'pusillanimous', 25: 'hawking:', 26: 'buds', 27: 'friction', 28: 'reed', 29: "sayin'", 30: 'grandé', 31: 'goes', 32: 'pained', 33: 'dumptruck', 34: 'ignorant', 35: 'moe-near-now', 36: 'bob', 37: "round's", 38: 'patrons:', 39: 'terror', 40: 'serve', 41: 'cab', 42: 'coat', 43: 'tense', 44: 'ripping', 45: 'rich', 46: 'mediterranean', 47: 'shriners', 48: 'accelerating', 49: 'coincidentally', 50: 'better', 51: 'alibi', 52: 'banquo', 53: 'twentieth', 54: 'drinking', 55: 'alec_baldwin:', 56: 'intruding', 57: 'realizing', 58: 'al', 59: 'f-l-a-n-r-d-s', 60: 'fun', 61: 'prince', 62: 'placed', 63: 'tapping', 64: 'country-fried', 65: 'guff', 66: 'waltz', 67: 'maximum', 68: 'craphole', 69: 'clincher', 70: 'perplexed', 71: 'horrified', 72: 'raise', 73: 'grow', 74: 'yours', 75: 'grunt', 76: 'lookalike:', 77: 'raccoons', 78: 'out', 79: "nick's", 80: 'recipe', 81: 'heads', 82: '||return||', 83: 'whatchamacallit', 84: 'fixed', 85: "battin'", 86: "barney's", 87: 'doll', 88: 'octa-', 89: 'drinks', 90: 'forty', 91: 'artist', 92: 'defected', 93: 'darjeeling', 94: 'low-life', 95: 'hooray', 96: 'victim', 97: 'intelligent', 98: 'hurt', 99: 'bidet', 100: 'hate-hugs', 101: 'known', 102: 'entertainer', 103: 'wants', 104: "tap-pullin'", 105: 'hospital', 106: 'grain', 107: 'forehead', 108: "'now", 109: 'savagely', 110: 'pernt', 111: 'mariah', 112: 'load', 113: 'mount', 114: "doin'", 115: "b-52's:", 116: 'popped', 117: 'feel', 118: 'worldly', 119: 'gator:', 120: 'carey', 121: 'taking', 122: 'heavens', 123: 'eight-year-old', 124: "kearney's_dad:", 125: 'sobo', 126: 'hydrant', 127: 'high-definition', 128: "bar's", 129: 'ees', 130: 'brandy', 131: 'squeals', 132: 'focus', 133: "i'd'a", 134: 'groveling', 135: 'subscriptions', 136: "dyin'", 137: 'hate', 138: "hawkin'", 139: 'domed', 140: '70', 141: 'apron', 142: 'fountain', 143: 'occasional', 144: 'longer', 145: 'towed', 146: 'however', 147: 'kemi:', 148: 'danish', 149: "aren'tcha", 150: 'hold', 151: 'lifestyle', 152: 'academy', 153: "'bout", 154: 'gentle', 155: 'takeaway', 156: 'dee-fense', 157: 'maggie', 158: 'dumbest', 159: 'bills', 160: "ya'", 161: 'courthouse', 162: 'motor', 163: 'moe_recording:', 164: 'mike', 165: 'crowds', 166: 'barf', 167: 'diapers', 168: 'agh', 169: 'scrutinizes', 170: "wait'll", 171: 'ideal', 172: 'amount', 173: 'busy', 174: 'l', 175: 'defiantly', 176: 'drummer', 177: 'sees/', 178: 'dean', 179: 'sits', 180: 'bourbon', 181: 'tonight', 182: 'tony', 183: 'sudden', 184: 'rafters', 185: 'office', 186: 'elephants', 187: 'achem', 188: 'conspiratorial', 189: 'metal', 190: 'european', 191: 'grudgingly', 192: 'hobo', 193: 'was', 194: 'briefly', 195: "playin'", 196: 'murmur', 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[ 1.64661085e-08 3.69654032e-07 5.95131979e-08 ..., 1.09520101e-07
2.15883826e-08 9.04724402e-08]
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[ 3.52308960e-10 2.84852281e-10 8.20703616e-10 ..., 2.97590441e-09
6.41255449e-09 4.75794959e-09]
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[ 2.65621775e-10 1.16072933e-11 7.72505254e-11 ..., 1.30296940e-10
1.65826991e-10 4.23232205e-09]
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[ 2.91476288e-11 7.11458323e-12 8.98449093e-11 ..., 2.08758948e-11
3.05958973e-11 3.84873633e-10]
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[ 2.42647658e-09 3.04790887e-10 1.09834745e-10 ..., 2.21271751e-10
9.05576059e-10 2.12762208e-09]
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9.25219013e-09 7.01181169e-10]
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[ 2.53953508e-11 7.73357073e-12 2.33654138e-11 ..., 3.17398430e-12
1.20007326e-09 1.52651199e-12]
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[ 3.31376038e-10 3.30827067e-11 1.49219448e-09 ..., 3.82329064e-11
2.51073135e-10 1.65566644e-10]
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[ 1.39352563e-09 4.66300748e-11 1.41295572e-10 ..., 5.93872694e-12
3.35664163e-10 4.11654405e-10]
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1.97425631e-08 1.78838638e-10]
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[ 1.72210812e-09 1.37809775e-09 3.20094506e-11 ..., 2.14997620e-11
3.60347663e-09 1.54652194e-10]
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[ 3.22706684e-09 1.20428079e-09 1.47258178e-11 ..., 4.38814055e-11
6.78047529e-09 1.79155080e-10]
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[ 1.28949171e-10 3.51256357e-10 9.61549868e-11 ..., 6.80924907e-14
7.66086326e-12 9.78228262e-11]
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[ 2.85477508e-05 3.94182564e-07 2.92653617e-08 ..., 5.38951150e-09
3.02222887e-08 1.21847927e-06]
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[ 2.02576994e-05 1.87602045e-10 2.10036197e-10 ..., 1.04038937e-10
1.12330173e-10 2.38750371e-08]
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[ 1.51487802e-05 6.44900855e-10 7.04697620e-11 ..., 6.01172757e-12
1.97683224e-11 1.85040272e-09]
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[ 4.67127655e-03 3.19908249e-05 2.07090594e-08 ..., 1.67758909e-08
7.18434364e-07 6.75870808e-07]
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[ 4.30404795e-10 2.52208983e-11 1.17761148e-13 ..., 3.69885261e-14
3.02258733e-13 2.67090777e-12]
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[ 1.49488005e-06 5.61753723e-07 1.18453736e-08 ..., 5.14980891e-10
1.73783932e-10 2.70845568e-08]
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[ 1.16171485e-07 2.80333978e-08 4.29281499e-10 ..., 5.20960323e-11
1.84014210e-11 2.56925148e-09]
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4.15050210e-14 9.35443021e-13]
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[ 1.76397748e-07 1.20912000e-06 4.73979134e-10 ..., 4.38735409e-10
3.22327234e-11 9.29584409e-10]
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[ 1.05943320e-06 1.65678848e-05 1.66826020e-08 ..., 1.17493224e-08
1.60131108e-08 2.49536839e-07]
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[ 8.07063572e-09 1.78977473e-07 1.50434616e-08 ..., 8.16951645e-11
1.33277622e-09 8.34767366e-09]
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[ 9.80761916e-10 1.28633859e-08 3.91427273e-11 ..., 6.02756248e-11
5.33408984e-10 5.13499678e-08]
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1.15748699e-05 2.36739052e-05]
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[ 2.55240273e-11 5.59793288e-11 4.72853839e-11 ..., 2.48243223e-12
6.85614454e-10 8.79523954e-10]
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[ 1.07698618e-07 4.69236738e-08 1.40113721e-09 ..., 3.07765508e-10
4.92382579e-09 6.23918339e-08]
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[ 7.41474970e-09 1.10719249e-07 1.41054252e-10 ..., 2.58828427e-11
8.36957437e-09 3.41541337e-08]
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[ 3.96949360e-07 1.74654235e-06 1.26408803e-08 ..., 1.26490090e-08
2.03619294e-07 8.04371894e-07]
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[ 4.24561279e-07 4.95869654e-06 1.17915853e-07 ..., 7.72299913e-08
4.14164879e-06 3.46229604e-06]
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[ 2.05515230e-10 5.99284178e-09 3.55517803e-11 ..., 5.32046129e-10
3.57931675e-08 1.85161930e-09]
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[ 1.06074356e-06 2.24730883e-07 4.13267891e-08 ..., 5.01639761e-07
5.88085925e-07 4.66923296e-07]
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[ 2.63049941e-07 3.09490034e-08 2.10095344e-10 ..., 1.04864199e-07
9.84110784e-08 1.07858487e-08]
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[ 9.59458589e-07 1.12535847e-08 1.56862873e-11 ..., 9.88425519e-09
1.02433777e-08 2.37445406e-08]
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[ 1.48495207e-08 3.37259216e-08 1.27442862e-10 ..., 4.87033347e-09
2.03285992e-08 4.03147649e-09]
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[ 2.79796103e-10 3.43644313e-09 1.86705859e-12 ..., 3.65577790e-10
1.52929100e-08 1.24230071e-10]
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[ 3.42440245e-07 5.72798456e-07 1.54050800e-11 ..., 3.69997565e-07
1.37530515e-05 3.94627939e-08]
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[ 6.76039402e-09 3.63306150e-08 6.36275060e-11 ..., 1.73524040e-08
2.36603242e-07 4.38454073e-08]
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[ 2.45694764e-07 4.72054529e-09 2.88734014e-09 ..., 4.59186595e-06
1.43791103e-05 1.18543960e-06]
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[ 6.13711904e-07 2.83346282e-07 1.23533919e-10 ..., 4.04708125e-08
5.72586245e-08 1.72120338e-07]
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[ 1.22292540e-05 2.82659475e-03 1.75341597e-07 ..., 6.43698399e-07
9.25400818e-04 1.34442348e-06]
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[ 6.13365216e-07 2.40517039e-07 2.11906848e-09 ..., 3.53866199e-08
1.50274630e-07 9.07326623e-08]
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[ 3.73320859e-08 1.18076233e-08 3.25933419e-10 ..., 1.74763648e-10
4.81667882e-07 1.97878194e-08]
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[ 5.33154093e-07 3.77761911e-09 7.34005356e-11 ..., 4.23449453e-09
7.79447973e-08 1.88641991e-07]
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[ 3.25277725e-08 2.92686125e-10 9.41236949e-11 ..., 6.16373549e-11
8.56967741e-09 1.53422786e-08]
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[ 1.31297975e-05 1.01908615e-08 1.18040768e-08 ..., 1.10871907e-08
1.71009148e-07 2.07088604e-08]
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6.82244308e-06 1.62732174e-08]
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[ 2.87556134e-07 3.04271914e-08 1.42631492e-07 ..., 1.44571473e-07
2.56709882e-06 1.56943287e-07]
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[ 3.72196141e-09 1.13852749e-09 2.70018868e-10 ..., 6.21089953e-08
6.07880466e-08 9.64749006e-07]
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1.78328197e-09 9.44445873e-08]
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1.99397664e-06 1.15154435e-07]
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[ 1.03103359e-09 1.97185468e-09 3.54583966e-11 ..., 1.98459540e-10
2.48446402e-10 1.68250702e-09]
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[ 1.28469999e-08 1.10991252e-08 1.91693848e-11 ..., 2.57642629e-10
9.93405469e-10 1.32814493e-09]
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[ 8.60505533e-09 7.24838571e-08 8.56723747e-10 ..., 5.08909759e-09
1.27867213e-06 8.26211064e-08]
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[ 3.16674544e-08 2.91753750e-08 1.40586209e-08 ..., 7.38055617e-10
4.14455542e-07 1.69899117e-09]
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[ 3.47479727e-07 1.19307344e-06 1.31632660e-08 ..., 4.17983195e-08
1.66710215e-05 2.74941101e-08]
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[ 2.70515876e-08 3.94946618e-08 1.54487889e-08 ..., 1.10793348e-08
5.13097575e-07 1.41913770e-08]
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[ 6.98470032e-11 2.99820657e-09 3.84172312e-11 ..., 4.44885212e-10
1.79448523e-09 1.26021538e-09]
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[ 3.67844644e-09 2.46462886e-08 3.52073787e-10 ..., 1.39900563e-10
2.27059616e-08 8.13044139e-08]
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[ 6.12024635e-08 5.46654499e-09 4.80816532e-11 ..., 6.61999400e-10
2.27332180e-08 1.80573192e-08]
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[ 5.70520520e-08 1.01636267e-07 9.46031933e-11 ..., 4.08504866e-06
3.63554636e-06 1.91265926e-07]
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[ 4.93047381e-10 1.89916936e-08 1.07303220e-11 ..., 2.96444691e-09
1.45653826e-06 4.10723205e-10]
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5.44144882e-07 3.51400544e-08]
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[ 1.09402363e-08 3.03064382e-07 4.57513361e-10 ..., 1.52143770e-07
5.83165865e-06 6.98886879e-07]
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4.18827057e-07 9.86649962e-09]
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[ 2.85457400e-12 1.33539180e-10 2.05367285e-13 ..., 1.41100510e-12
6.73808015e-12 1.32111863e-10]
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4.69927066e-08 8.70880790e-09]
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[ 2.21632532e-10 1.02285199e-07 1.41143312e-11 ..., 6.00654804e-10
2.58458832e-09 4.88295404e-10]
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[ 8.22059860e-07 2.72556135e-05 3.34703607e-08 ..., 2.10094868e-07
2.75637177e-07 2.16344233e-06]
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[ 1.74733810e-08 2.13909473e-07 1.78185730e-10 ..., 5.54777280e-10
1.68623493e-09 3.00579228e-09]
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[ 2.88139059e-08 4.48841381e-11 1.80617171e-11 ..., 8.03557470e-13
4.95539211e-13 3.59471372e-12]
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[ 3.98074889e-07 8.46793284e-08 1.89854923e-10 ..., 1.25509186e-10
3.46704609e-10 1.30500022e-09]
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[ 1.16296794e-09 8.18914181e-09 2.05875456e-11 ..., 4.01806539e-11
4.04111189e-09 2.36653280e-10]
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[ 1.93688143e-12 6.21829194e-12 1.48646395e-12 ..., 5.77724175e-13
2.84183250e-11 5.86398009e-13]
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[ 3.27008798e-09 1.09585748e-07 3.63929709e-09 ..., 1.43567602e-09
2.62004409e-08 2.62371325e-09]
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[ 3.74297437e-09 2.23991970e-06 1.98043734e-10 ..., 2.37864373e-09
1.38956875e-06 1.62362390e-08]
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[ 8.54858229e-08 1.92771995e-06 7.42853956e-08 ..., 3.67649217e-10
1.66407972e-07 4.93638652e-07]
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[ 1.96376909e-03 9.24203675e-07 5.19978130e-07 ..., 8.97230450e-08
1.20718283e-07 5.61868865e-06]
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[ 1.24279623e-11 1.26994092e-11 8.55063589e-12 ..., 3.34381177e-12
3.00325064e-12 2.57344580e-12]
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[ 1.48021127e-05 8.94922232e-06 3.97271760e-05 ..., 3.75413663e-07
4.90021819e-07 8.92786261e-07]
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[ 3.75078280e-08 3.73779972e-06 8.97179664e-09 ..., 7.86262717e-08
1.84624582e-07 4.28595612e-08]
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[ 2.96407438e-06 1.65370584e-05 4.71054662e-09 ..., 2.34873610e-07
1.51518435e-07 7.35918138e-06]
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2.40667930e-07 1.01897683e-06]
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[ 1.76205916e-07 2.57606310e-08 4.77564488e-10 ..., 3.50280089e-08
1.08724061e-08 1.54258089e-07]
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'helen', 2498: 'thought', 2499: 'krabappel', 2500: 'half-beer', 2501: 'percent', 2502: 'idiot', 2503: 'crumble', 2504: 'barbed', 2505: 'makes', 2506: "floatin'", 2507: 'awfully', 2508: 'course', 2509: 'charming', 2510: 'opens', 2511: 'lights', 2512: 'unhook', 2513: 'boozebag', 2514: 'founded', 2515: 'treat', 2516: 'life-partner', 2517: 'f', 2518: 'hellhole', 2519: 'wise', 2520: 'prices', 2521: 'horribilis', 2522: 'bronco', 2523: 'club', 2524: 'tastes', 2525: "moe's_thoughts:", 2526: 'shotgun', 2527: 'kearney_zzyzwicz:', 2528: 'victory', 2529: 'souvenir', 2530: 'rotten', 2531: 'bank', 2532: 'lady_duff:', 2533: 'sprawl', 2534: 'duffman:', 2535: 'position', 2536: 'ragtime', 2537: 'till', 2538: 'sunk', 2539: 'crony', 2540: 'hounds', 2541: 'gay', 2542: 'fridge', 2543: 'sigh', 2544: 'otherwise', 2545: "number's", 2546: 'vampire', 2547: 'trees', 2548: 'gibson', 2549: 'offer', 2550: 'serum', 2551: 'investment', 2552: 'dictating', 2553: 'sobs', 2554: 'months', 2555: 'sure', 2556: 'marriage', 2557: 'fixes', 2558: 'glyco-load', 2559: 'hall', 2560: 'sec_agent_#1:', 2561: 'pointedly', 2562: 'wealthy', 2563: 'cracked', 2564: 'mayan', 2565: 'in-ground', 2566: 'wells', 2567: 'forty-seven', 2568: 'discuss', 2569: 'wild', 2570: 'poking', 2571: 'disappointed', 2572: 'can', 2573: 'getcha', 2574: 'harvey', 2575: 'illegally', 2576: 'slyly', 2577: 'las', 2578: 'juke', 2579: 'inserts', 2580: "games'd", 2581: 'lemonade', 2582: 'man_with_crazy_beard:', 2583: 'gabriel:', 2584: 'moe', 2585: 'st', 2586: 'curiosity', 2587: 'u', 2588: 'fudd', 2589: 'gorgeous', 2590: 'cheap', 2591: 'eliminate', 2592: 'dimly', 2593: 'contemplates', 2594: 'leonard', 2595: 'show-off', 2596: 'introduce', 2597: 'windshield', 2598: 'complaint', 2599: 'wordloaf', 2600: 'ronstadt', 2601: 'poplar', 2602: 'sports', 2603: "time's", 2604: 'competitive', 2605: "industry's", 2606: 'park', 2607: 'cans', 2608: 'goodnight', 2609: '